pyr0ball
98695b00f0
Core trust scoring: - Five metadata signals (account age, feedback count/ratio, price vs market, category history), composited 0–100 - CV-based price signal suppression for heterogeneous search results (e.g. mixed laptop generations won't false-positive suspicious_price) - Expanded scratch/dent title detection: evasive redirects, functional problem phrases, DIY/repair indicators - Hard filters: new_account, established_bad_actor - Soft flags: low_feedback, suspicious_price, duplicate_photo, scratch_dent, long_on_market, significant_price_drop Search & filtering: - Browse API adapter (up to 200 items/page) + Playwright scraper fallback - OR-group query expansion for comprehensive variant coverage - Must-include (AND/ANY/groups), must-exclude, category, price range filters - Saved searches with full filter round-trip via URL params Seller enrichment: - Background BTF /itm/ scraping for account age (Kasada-safe headed Chromium) - On-demand enrichment: POST /api/enrich + ListingCard ↻ button - Category history derived from Browse API categories field (free, no extra calls) - Shopping API GetUserProfile inline enrichment for API adapter Market comps: - eBay Marketplace Insights API with Browse API fallback (catches 403 + 404) - Comps prioritised in ThreadPoolExecutor (submitted first) Infrastructure: - Staging DB fields: times_seen, first_seen_at, price_at_first_seen, category_name - Migrations 004 (staging tracking) + 005 (listing category) - eBay webhook handler stub - Cloud compose stack (compose.cloud.yml) - Vue frontend: search store, saved searches store, ListingCard, filter sidebar Docs: - README fully rewritten to reflect MVP status + full feature documentation - Roadmap table linked to all 13 Forgejo issues
85 lines
3 KiB
Python
85 lines
3 KiB
Python
"""
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Build eBay-compatible boolean search queries from OR groups.
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eBay honors parenthetical OR groups in the _nkw search parameter:
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(term1,term2,term3) → must contain at least one of these terms
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-term / -"phrase" → must NOT contain this term / phrase
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space between groups → implicit AND
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expand_queries() generates one eBay query per term in the smallest OR group,
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using eBay's OR syntax for all remaining groups. This guarantees coverage even
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if eBay's relevance ranking would suppress some matches in a single combined query.
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Example:
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base = "GPU"
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or_groups = [["16gb","24gb","40gb","48gb"], ["nvidia","quadro","rtx","geforce","titan"]]
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→ 4 queries (one per memory size, brand group as eBay OR):
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"GPU 16gb (nvidia,quadro,rtx,geforce,titan)"
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"GPU 24gb (nvidia,quadro,rtx,geforce,titan)"
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"GPU 40gb (nvidia,quadro,rtx,geforce,titan)"
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"GPU 48gb (nvidia,quadro,rtx,geforce,titan)"
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"""
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from __future__ import annotations
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def _group_to_ebay(group: list[str]) -> str:
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"""Convert a list of alternatives to an eBay OR clause."""
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clean = [t.strip() for t in group if t.strip()]
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if not clean:
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return ""
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if len(clean) == 1:
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return clean[0]
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return f"({','.join(clean)})"
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def build_ebay_query(base_query: str, or_groups: list[list[str]]) -> str:
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"""
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Build a single eBay _nkw query string using eBay's parenthetical OR syntax.
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Exclusions are handled separately via SearchFilters.must_exclude.
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"""
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parts = [base_query.strip()]
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for group in or_groups:
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clause = _group_to_ebay(group)
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if clause:
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parts.append(clause)
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return " ".join(p for p in parts if p)
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def expand_queries(base_query: str, or_groups: list[list[str]]) -> list[str]:
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"""
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Expand OR groups into one eBay query per term in the smallest group,
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using eBay's OR syntax for all remaining groups.
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This guarantees every term in the pivot group is explicitly searched,
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which prevents eBay's relevance engine from silently skipping rare variants.
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Falls back to a single query when there are no OR groups.
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"""
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if not or_groups:
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return [base_query.strip()]
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# Pivot on the smallest group to minimise the number of Playwright calls
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smallest_idx = min(range(len(or_groups)), key=lambda i: len(or_groups[i]))
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pivot = or_groups[smallest_idx]
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rest = [g for i, g in enumerate(or_groups) if i != smallest_idx]
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queries = []
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for term in pivot:
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q = build_ebay_query(base_query, [[term]] + rest)
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queries.append(q)
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return queries
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def parse_groups(raw: str) -> list[list[str]]:
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"""
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Parse a Groups-mode must_include string into nested OR groups.
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Format: comma separates groups (AND), pipe separates alternatives within a group (OR).
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"16gb|24gb|48gb, nvidia|rtx|geforce"
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→ [["16gb","24gb","48gb"], ["nvidia","rtx","geforce"]]
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"""
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groups = []
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for chunk in raw.split(","):
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alts = [t.strip().lower() for t in chunk.split("|") if t.strip()]
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if alts:
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groups.append(alts)
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return groups
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